SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering
Jingxuan Tu, Eben Holderness, Marco Maru, Simone Conia, Kyeongmin Rim, Kelley Lynch, Richard Brutti, Roberto Navigli, James Pustejovsky
Abstract
In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning. Particularly, given the intuition that textual and visual information mutually inform each other for semantic reasoning, we formulate a Competence-based Question Answering challenge, designed to involve rich semantic annotation and aligned text-video objects. The task is to answer questions from a collection of cooking recipes and videos, where each question belongs to a “question family” reflecting a specific reasoning competence. The data and task result is publicly available.- Anthology ID:
- 2022.semeval-1.176
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1244–1255
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.176
- DOI:
- 10.18653/v1/2022.semeval-1.176
- Cite (ACL):
- Jingxuan Tu, Eben Holderness, Marco Maru, Simone Conia, Kyeongmin Rim, Kelley Lynch, Richard Brutti, Roberto Navigli, and James Pustejovsky. 2022. SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1244–1255, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering (Tu et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.semeval-1.176.pdf
- Data
- GLUE, Visual Question Answering, WSC